Inclusive Urban Regeneration with Citizens and Stakeholders: From Living Labs to the URBiNAT CoP
Bibliographic record
Abstract
Abstract In recent decades, many city authorities have been implementing strategies for the development of urban regeneration in their central areas. Most of these processes aim to improve the use of public space, and are often to be found in historic areas and waterfronts. The aim of this text is to put forward an alternative urban regeneration plan which focuses on the peripheral areas of cities, areas which were often built as neighbourhoods of social housing, and which now face environmental challenges as well as social and economic ones. To this end, the URBiNAT H2020 project is promoting inclusive urban regeneration that engages citizens and stakeholders in all the stages of the co-creation process. The overall objective is to implement a cluster of human-centred, nature-based solutions (NBS) in order to create Healthy Corridors that bring together both material and immaterial solutions that will impact the environment and the wellbeing of the community. The activation of Living Labs in the seven URBiNAT cities is building a Community of Practice so that knowledge can be shared with project partners, within the cities themselves, and with the public in the wider world. The intermediate results achieved in the pilot case studies validate the overall methodology and are helping us to identify lessons to be learnt and recommendations for the future.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".